| Autor: |
Da Cunha PA; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA., Nitusca D; Department of Biochemistry and Pharmacology, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania.; Center for Complex Networks Science, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania., Canto LMD; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA., Varghese RS; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA., Ressom HW; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA., Willey S; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.; Department of Surgery, Georgetown University Medical Center, Georgetown University, Washington, DC 20007, USA., Marian C; Department of Biochemistry and Pharmacology, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania.; Center for Complex Networks Science, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania., Haddad BR; Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA. |
| Abstrakt: |
Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices’ paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings. |
